In [74]:
plot_loss_curves(base_model_history_101_food_classes_10_percent)
!nvidia-smi
Mon Mar 13 22:02:51 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 530.30.02 Driver Version: 531.18 CUDA Version: 12.1 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 3060 L... On | 00000000:01:00.0 Off | N/A |
| N/A 52C P8 10W / N/A| 11MiB / 6144MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 17 G /Xwayland N/A |
| 0 N/A N/A 126 G /Xwayland N/A |
| 0 N/A N/A 618 G /Xwayland N/A |
| 0 N/A N/A 10403 G /Xwayland N/A |
+---------------------------------------------------------------------------------------+
import tensorflow as tf
2023-03-13 22:02:52.143172: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
# Import helper functions
from helper_functions import create_tensorboard_callback, plot_loss_curves, unzip_data, walk_through_dir, compare_historys
!wget https://storage.googleapis.com/ztm_tf_course/food_vision/101_food_classes_10_percent.zip
--2023-03-13 22:02:56-- https://storage.googleapis.com/ztm_tf_course/food_vision/101_food_classes_10_percent.zip Resolving storage.googleapis.com (storage.googleapis.com)... 142.251.41.48, 142.251.41.80, 172.217.1.16, ... Connecting to storage.googleapis.com (storage.googleapis.com)|142.251.41.48|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 1625420029 (1.5G) [application/zip] Saving to: ‘101_food_classes_10_percent.zip.1’ 101_food_classes_10 100%[===================>] 1.51G 54.5MB/s in 29s 2023-03-13 22:03:26 (52.9 MB/s) - ‘101_food_classes_10_percent.zip.1’ saved [1625420029/1625420029]
unzip_data("101_food_classes_10_percent.zip")
train_dir = "101_food_classes_10_percent/train"
test_dir = "101_food_classes_10_percent/test"
# Check directory
walk_through_dir("101_food_classes_10_percent")
There are 2 directories and 0 images in '101_food_classes_10_percent'. There are 101 directories and 0 images in '101_food_classes_10_percent/train'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/frozen_yogurt'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/pad_thai'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/crab_cakes'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/french_toast'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/cheese_plate'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/churros'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/pancakes'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/beef_carpaccio'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/escargots'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/tuna_tartare'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/dumplings'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/chocolate_cake'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/baby_back_ribs'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/fried_rice'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/pork_chop'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/shrimp_and_grits'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/fish_and_chips'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/eggs_benedict'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/foie_gras'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/chicken_wings'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/beignets'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/ramen'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/waffles'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/caesar_salad'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/breakfast_burrito'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/filet_mignon'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/lobster_bisque'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/panna_cotta'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/nachos'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/garlic_bread'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/deviled_eggs'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/guacamole'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/french_onion_soup'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/poutine'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/huevos_rancheros'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/bread_pudding'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/pulled_pork_sandwich'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/baklava'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/gyoza'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/club_sandwich'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/onion_rings'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/chocolate_mousse'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/miso_soup'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/steak'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/samosa'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/peking_duck'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/hot_and_sour_soup'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/ice_cream'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/pizza'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/oysters'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/fried_calamari'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/scallops'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/hamburger'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/seaweed_salad'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/spring_rolls'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/lasagna'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/takoyaki'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/donuts'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/cannoli'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/sashimi'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/grilled_salmon'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/cup_cakes'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/bruschetta'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/ceviche'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/creme_brulee'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/spaghetti_bolognese'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/beef_tartare'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/chicken_curry'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/macaroni_and_cheese'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/prime_rib'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/ravioli'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/carrot_cake'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/macarons'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/paella'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/gnocchi'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/sushi'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/hot_dog'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/tacos'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/clam_chowder'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/chicken_quesadilla'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/risotto'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/edamame'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/cheesecake'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/greek_salad'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/red_velvet_cake'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/caprese_salad'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/grilled_cheese_sandwich'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/falafel'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/apple_pie'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/beet_salad'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/mussels'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/bibimbap'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/spaghetti_carbonara'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/omelette'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/strawberry_shortcake'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/french_fries'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/tiramisu'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/lobster_roll_sandwich'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/hummus'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/pho'. There are 0 directories and 75 images in '101_food_classes_10_percent/train/croque_madame'. There are 101 directories and 0 images in '101_food_classes_10_percent/test'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/frozen_yogurt'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/pad_thai'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/crab_cakes'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/french_toast'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/cheese_plate'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/churros'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/pancakes'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/beef_carpaccio'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/escargots'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/tuna_tartare'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/dumplings'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/chocolate_cake'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/baby_back_ribs'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/fried_rice'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/pork_chop'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/shrimp_and_grits'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/fish_and_chips'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/eggs_benedict'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/foie_gras'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/chicken_wings'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/beignets'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/ramen'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/waffles'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/caesar_salad'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/breakfast_burrito'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/filet_mignon'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/lobster_bisque'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/panna_cotta'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/nachos'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/garlic_bread'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/deviled_eggs'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/guacamole'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/french_onion_soup'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/poutine'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/huevos_rancheros'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/bread_pudding'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/pulled_pork_sandwich'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/baklava'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/gyoza'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/club_sandwich'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/onion_rings'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/chocolate_mousse'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/miso_soup'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/steak'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/samosa'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/peking_duck'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/hot_and_sour_soup'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/ice_cream'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/pizza'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/oysters'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/fried_calamari'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/scallops'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/hamburger'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/seaweed_salad'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/spring_rolls'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/lasagna'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/takoyaki'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/donuts'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/cannoli'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/sashimi'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/grilled_salmon'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/cup_cakes'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/bruschetta'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/ceviche'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/creme_brulee'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/spaghetti_bolognese'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/beef_tartare'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/chicken_curry'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/macaroni_and_cheese'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/prime_rib'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/ravioli'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/carrot_cake'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/macarons'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/paella'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/gnocchi'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/sushi'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/hot_dog'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/tacos'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/clam_chowder'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/chicken_quesadilla'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/risotto'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/edamame'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/cheesecake'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/greek_salad'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/red_velvet_cake'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/caprese_salad'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/grilled_cheese_sandwich'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/falafel'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/apple_pie'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/beet_salad'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/mussels'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/bibimbap'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/spaghetti_carbonara'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/omelette'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/strawberry_shortcake'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/french_fries'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/tiramisu'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/lobster_roll_sandwich'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/hummus'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/pho'. There are 0 directories and 250 images in '101_food_classes_10_percent/test/croque_madame'.
# Setup data inputs
import tensorflow as tf
IMG_SIZE = (224, 224)
train_data_all_10_percent = tf.keras.preprocessing.image_dataset_from_directory(train_dir,
image_size=IMG_SIZE,
label_mode="categorical")
test_data = tf.keras.preprocessing.image_dataset_from_directory(test_dir,
image_size=IMG_SIZE,
label_mode="categorical",
shuffle=False)
Found 7575 files belonging to 101 classes.
2023-03-13 22:03:43.809282: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:961] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node Your kernel may have been built without NUMA support. 2023-03-13 22:03:43.955861: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:961] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node Your kernel may have been built without NUMA support. 2023-03-13 22:03:43.955913: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:961] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node Your kernel may have been built without NUMA support. 2023-03-13 22:03:43.959364: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2023-03-13 22:03:43.964089: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:961] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node Your kernel may have been built without NUMA support. 2023-03-13 22:03:43.964189: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:961] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node Your kernel may have been built without NUMA support. 2023-03-13 22:03:43.964215: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:961] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node Your kernel may have been built without NUMA support. 2023-03-13 22:03:46.940174: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:961] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node Your kernel may have been built without NUMA support. 2023-03-13 22:03:46.940272: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:961] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node Your kernel may have been built without NUMA support. 2023-03-13 22:03:46.940281: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Could not identify NUMA node of platform GPU id 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2023-03-13 22:03:46.940312: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:961] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node Your kernel may have been built without NUMA support. 2023-03-13 22:03:46.940374: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3433 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6
Found 25250 files belonging to 101 classes.
Steps:
# Creat checkpoint callback
checkpoint_path = "101_classes_10_percent_data_model_checkpoint/checkpoints.ckpt"
checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
save_weights_only=True,
monitor="val_accuracy",
save_best_only=True)
# Create data augmentation layer
from tensorflow.keras import layers
from tensorflow.keras import Sequential
data_augmentation = Sequential([
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.2),
layers.RandomHeight(0.2),
layers.RandomWidth(0.2),
layers.RandomZoom(0.2),
# layers.Rescaling(1/255.) # Rescale images required for ResNet50v2
], name="data_augmentation")
# Setup the base mode and freeze its layers (to extract features)
base_model = tf.keras.applications.EfficientNetB0(include_top=False)
base_model.trainable = False
# Setup model architecture with trainable top layres
inputs = layers.Input(shape=(224, 224, 3), name="input_layer")
x = data_augmentation(inputs) # Augment images during training
x = base_model(x, training=False) # put base model in inference mode to set weights frozen
x = layers.GlobalAveragePooling2D(name="global_avg_pool_layer")(x)
outputs = layers.Dense(len(train_data_all_10_percent.class_names), activation="softmax", name="output_layer")(x)
model = tf.keras.Model(inputs, outputs)
# Compile the model
model.compile(loss="categorical_crossentropy",
optimizer=tf.keras.optimizers.Adam(),
metrics=["accuracy"])
model.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_layer (InputLayer) [(None, 224, 224, 3)] 0
data_augmentation (Sequenti (None, 224, 224, 3) 0
al)
efficientnetb0 (Functional) (None, None, None, 1280) 4049571
global_avg_pool_layer (Glob (None, 1280) 0
alAveragePooling2D)
output_layer (Dense) (None, 101) 129381
=================================================================
Total params: 4,178,952
Trainable params: 129,381
Non-trainable params: 4,049,571
_________________________________________________________________
# Fit the model
base_model_history_101_food_classes_10_percent = model.fit(train_data_all_10_percent,
epochs=5,
validation_data=test_data,
validation_steps=int(0.15*len(test_data)),
callbacks=[checkpoint_callback, create_tensorboard_callback(dir_name="transfer_learning",
experiment_name="base_model_history_101_food_classes_10_percent")])
Saving TensorBoard log files to: transfer_learning/base_model_history_101_food_classes_10_percent/20230313-220351 Epoch 1/5
2023-03-13 22:04:02.288733: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8700
1/237 [..............................] - ETA: 58:45 - loss: 4.7971 - accuracy: 0.0000e+00
2023-03-13 22:04:06.693938: I tensorflow/stream_executor/cuda/cuda_blas.cc:1786] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
237/237 [==============================] - 80s 278ms/step - loss: 3.3773 - accuracy: 0.2750 - val_loss: 2.4317 - val_accuracy: 0.4828 Epoch 2/5 237/237 [==============================] - 54s 229ms/step - loss: 2.2031 - accuracy: 0.5002 - val_loss: 2.0229 - val_accuracy: 0.5204 Epoch 3/5 237/237 [==============================] - 51s 217ms/step - loss: 1.8199 - accuracy: 0.5642 - val_loss: 1.8875 - val_accuracy: 0.5334 Epoch 4/5 237/237 [==============================] - 49s 208ms/step - loss: 1.6030 - accuracy: 0.6141 - val_loss: 1.7667 - val_accuracy: 0.5564 Epoch 5/5 237/237 [==============================] - 48s 205ms/step - loss: 1.4552 - accuracy: 0.6430 - val_loss: 1.7681 - val_accuracy: 0.5501
# Evaluate on whole test dataset
feature_extraction_results = model.evaluate(test_data)
feature_extraction_results
790/790 [==============================] - 49s 61ms/step - loss: 1.5844 - accuracy: 0.5837
[1.5844230651855469, 0.58368319272995]
plot_loss_curves(base_model_history_101_food_classes_10_percent)
# unfreeze all of the layers in the base model
base_model.trainable = True
# Refreeze every layer except last 5
for layer in base_model.layers[:-5]:
layer.trainable = False
for i, layer in enumerate(base_model.layers[-10:]):
print(i, layer.name, layer.trainable)
0 block7a_se_squeeze False 1 block7a_se_reshape False 2 block7a_se_reduce False 3 block7a_se_expand False 4 block7a_se_excite False 5 block7a_project_conv True 6 block7a_project_bn True 7 top_conv True 8 top_bn True 9 top_activation True
# Recompiling model with lower learning_rate
model.compile(loss="categorical_crossentropy",
optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
metrics=["accuracy"])
model.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_layer (InputLayer) [(None, 224, 224, 3)] 0
data_augmentation (Sequenti (None, 224, 224, 3) 0
al)
efficientnetb0 (Functional) (None, None, None, 1280) 4049571
global_avg_pool_layer (Glob (None, 1280) 0
alAveragePooling2D)
output_layer (Dense) (None, 101) 129381
=================================================================
Total params: 4,178,952
Trainable params: 910,821
Non-trainable params: 3,268,131
_________________________________________________________________
# Fit the model again
initial_epochs = 5
history_fine_tuned_base_model_101_food_classes_10_percent = model.fit(train_data_all_10_percent,
epochs=initial_epochs + 5,
validation_data=test_data,
validation_steps=int(0.15 * len(test_data)),
initial_epoch=base_model_history_101_food_classes_10_percent.epoch[-1],
callbacks=[create_tensorboard_callback(dir_name="transfer_learning",
experiment_name="history_fine_tuned_base_model_101_food_classes_10_percent")])
Saving TensorBoard log files to: transfer_learning/history_fine_tuned_base_model_101_food_classes_10_percent/20230313-221816 Epoch 5/10 237/237 [==============================] - 47s 174ms/step - loss: 1.2255 - accuracy: 0.6796 - val_loss: 1.7484 - val_accuracy: 0.5434 Epoch 6/10 237/237 [==============================] - 39s 164ms/step - loss: 1.0948 - accuracy: 0.7083 - val_loss: 1.7466 - val_accuracy: 0.5416 Epoch 7/10 237/237 [==============================] - 39s 165ms/step - loss: 1.0149 - accuracy: 0.7291 - val_loss: 1.7797 - val_accuracy: 0.5437 Epoch 8/10 237/237 [==============================] - 40s 169ms/step - loss: 0.9560 - accuracy: 0.7442 - val_loss: 1.7639 - val_accuracy: 0.5408 Epoch 9/10 237/237 [==============================] - 38s 161ms/step - loss: 0.8910 - accuracy: 0.7603 - val_loss: 1.7392 - val_accuracy: 0.5477 Epoch 10/10 237/237 [==============================] - 37s 158ms/step - loss: 0.8374 - accuracy: 0.7674 - val_loss: 1.7335 - val_accuracy: 0.5548
# Evaluate on whole dataset
all_classes_10_percent_fine_tune_results = model.evaluate(test_data)
all_classes_10_percent_fine_tune_results
790/790 [==============================] - 47s 60ms/step - loss: 1.4837 - accuracy: 0.6080
[1.4836808443069458, 0.6080396175384521]
# Compare the histories with fine tuning
compare_historys(original_history=base_model_history_101_food_classes_10_percent,
new_history=history_fine_tuned_base_model_101_food_classes_10_percent,
initial_epochs=5)
As we can see, after fine tuning, validation loss and accuracy stays flat while training values starts improving.
to use it in extrenal applications
# Save our model in h5 format
model.save("FastAPI_app/model/fine_tuned_model_v1.h5")
# Load and evaluate saved model
loaded_model = tf.keras.models.load_model("FastAPI_app/model/fine_tuned_model_v1.h5")
# Evaluate loaded models
loaded_model_results = loaded_model.evaluate(test_data)
loaded_model_results
790/790 [==============================] - 42s 51ms/step - loss: 1.4837 - accuracy: 0.6082
[1.4836807250976562, 0.6081584095954895]
# Model results before loading
all_classes_10_percent_fine_tune_results
[1.4836808443069458, 0.6080396175384521]
# Make predictions with model
preds_probs = loaded_model.predict(test_data, verbose=1) # set verbosity to see how log is left
790/790 [==============================] - 45s 56ms/step
# How many predictions are there
len(preds_probs)
25250
# Shapes of out predictions
preds_probs.shape
(25250, 101)
# let's see what first 10 predictions look like
preds_probs[:10]
array([[2.13262409e-01, 3.74424621e-04, 1.47300526e-01, ...,
1.00088573e-05, 1.19277136e-02, 1.11554479e-02],
[8.28700900e-01, 6.14220639e-08, 5.82025561e-04, ...,
5.36216982e-03, 8.75272349e-07, 6.64483878e-06],
[7.16566741e-01, 3.80486576e-03, 9.46540292e-03, ...,
2.49304052e-04, 3.07775714e-04, 7.64966302e-04],
...,
[1.75064757e-01, 4.42263627e-06, 2.12276657e-03, ...,
2.64248643e-02, 2.05834187e-03, 1.07281754e-04],
[1.79688051e-01, 3.12993274e-04, 3.71866375e-01, ...,
1.75711684e-04, 1.91263462e-04, 1.12177455e-04],
[6.95914507e-01, 5.97044789e-07, 1.16489735e-03, ...,
5.06197801e-03, 2.16848321e-06, 4.51417465e-04]], dtype=float32)
preds_probs[0], max(preds_probs[0]), sum(preds_probs[0])
(array([2.13262409e-01, 3.74424621e-04, 1.47300526e-01, 1.59269959e-07,
5.91850358e-06, 3.17156633e-07, 1.20911223e-04, 8.32445585e-06,
2.35058926e-02, 4.98985522e-04, 5.41419979e-07, 5.36608877e-06,
4.99380636e-04, 3.42688111e-07, 9.79942549e-03, 1.10681122e-03,
7.09556889e-06, 1.90289458e-03, 4.27856995e-03, 6.41824299e-05,
1.63555983e-03, 1.23004947e-05, 3.44534026e-04, 9.38770390e-05,
1.00595318e-03, 4.63977084e-03, 1.50340248e-03, 1.49832063e-04,
1.44488811e-01, 4.75591660e-04, 3.27649764e-06, 1.08300371e-03,
1.51802588e-03, 6.21370077e-07, 2.83269398e-02, 4.24230302e-06,
2.03907286e-04, 1.30680180e-03, 1.61797553e-02, 4.50966145e-05,
7.23947538e-04, 5.20048104e-03, 4.85921395e-04, 1.48963227e-05,
1.27142383e-04, 8.61562603e-06, 2.52367766e-03, 1.21461235e-04,
2.39577480e-06, 6.96821660e-02, 1.67483566e-04, 2.01781499e-04,
7.40272775e-02, 2.39406526e-02, 8.61716253e-05, 7.94744957e-03,
3.22495587e-02, 1.27961896e-02, 9.06321965e-03, 1.05639199e-04,
4.49018188e-07, 2.03054701e-03, 3.93103398e-02, 8.40474793e-04,
5.66966308e-04, 1.13091191e-04, 4.20528704e-05, 4.35997918e-03,
1.66725868e-03, 3.07308801e-04, 4.17302304e-04, 8.79140407e-06,
1.87891629e-03, 3.62995088e-05, 7.49920844e-04, 1.67468515e-05,
1.35470065e-04, 7.19289575e-03, 2.10717510e-04, 2.20792354e-04,
6.01738750e-04, 2.11758597e-04, 5.92641416e-04, 3.09142997e-05,
5.03251737e-04, 6.11881167e-02, 2.93529755e-03, 8.87527072e-04,
1.05828385e-05, 2.35582484e-04, 5.88492503e-06, 2.00948434e-05,
8.81854794e-04, 7.05208658e-05, 8.66177870e-05, 9.61464015e-04,
1.99602032e-03, 3.40731902e-04, 1.00088573e-05, 1.19277136e-02,
1.11554479e-02], dtype=float32),
0.21326241,
1.0000000030027394)
# we get one prediction probability per class
print(f"Number of prediction probabilities for sample 0: {len(preds_probs[0])}")
print(f"Waht prediction probability sample 0 looks like:\n {preds_probs[0]}")
print(f"The class with the heighest predicted probability by the model for sample 0: {preds_probs[0].argmax()}")
Number of prediction probabilities for sample 0: 101 Waht prediction probability sample 0 looks like: [2.13262409e-01 3.74424621e-04 1.47300526e-01 1.59269959e-07 5.91850358e-06 3.17156633e-07 1.20911223e-04 8.32445585e-06 2.35058926e-02 4.98985522e-04 5.41419979e-07 5.36608877e-06 4.99380636e-04 3.42688111e-07 9.79942549e-03 1.10681122e-03 7.09556889e-06 1.90289458e-03 4.27856995e-03 6.41824299e-05 1.63555983e-03 1.23004947e-05 3.44534026e-04 9.38770390e-05 1.00595318e-03 4.63977084e-03 1.50340248e-03 1.49832063e-04 1.44488811e-01 4.75591660e-04 3.27649764e-06 1.08300371e-03 1.51802588e-03 6.21370077e-07 2.83269398e-02 4.24230302e-06 2.03907286e-04 1.30680180e-03 1.61797553e-02 4.50966145e-05 7.23947538e-04 5.20048104e-03 4.85921395e-04 1.48963227e-05 1.27142383e-04 8.61562603e-06 2.52367766e-03 1.21461235e-04 2.39577480e-06 6.96821660e-02 1.67483566e-04 2.01781499e-04 7.40272775e-02 2.39406526e-02 8.61716253e-05 7.94744957e-03 3.22495587e-02 1.27961896e-02 9.06321965e-03 1.05639199e-04 4.49018188e-07 2.03054701e-03 3.93103398e-02 8.40474793e-04 5.66966308e-04 1.13091191e-04 4.20528704e-05 4.35997918e-03 1.66725868e-03 3.07308801e-04 4.17302304e-04 8.79140407e-06 1.87891629e-03 3.62995088e-05 7.49920844e-04 1.67468515e-05 1.35470065e-04 7.19289575e-03 2.10717510e-04 2.20792354e-04 6.01738750e-04 2.11758597e-04 5.92641416e-04 3.09142997e-05 5.03251737e-04 6.11881167e-02 2.93529755e-03 8.87527072e-04 1.05828385e-05 2.35582484e-04 5.88492503e-06 2.00948434e-05 8.81854794e-04 7.05208658e-05 8.66177870e-05 9.61464015e-04 1.99602032e-03 3.40731902e-04 1.00088573e-05 1.19277136e-02 1.11554479e-02] The class with the heighest predicted probability by the model for sample 0: 0
# Get the pred classes of each label
pred_classes = preds_probs.argmax(axis=1)
pred_classes[:10]
array([ 0, 0, 0, 8, 8, 78, 29, 46, 2, 0])
len(pred_classes)
25250
Now, compate pred_classes to test_data labels
# To get out test labels we need to unravel out test_data BatchDataset
y_label = []
for images, labels in test_data.unbatch():
y_label.append(labels.numpy().argmax()) # currently test labels look like: [0, 0, 0, 0, 1 ... , 0, 0]
y_label[:10] # look at first 10
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
len(y_label)
25250
loaded_model_results
[1.4836807250976562, 0.6081584095954895]
# trying scikit-learn's accuracy score function
from sklearn.metrics import accuracy_score
sklearn_accuracy = accuracy_score(y_true=y_label, y_pred=pred_classes)
sklearn_accuracy
0.6081584158415841
from helper_functions import make_confusion_matrix
import itertools
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix
#Need to make some changes in confusion matrix
def make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15, norm=False, savefig=False):
"""Makes a labelled confusion matrix comparing predictions and ground truth labels.
If classes is passed, confusion matrix will be labelled, if not, integer class values
will be used.
Args:
y_true: Array of truth labels (must be same shape as y_pred).
y_pred: Array of predicted labels (must be same shape as y_true).
classes: Array of class labels (e.g. string form). If `None`, integer labels are used.
figsize: Size of output figure (default=(10, 10)).
text_size: Size of output figure text (default=15).
norm: normalize values or not (default=False).
savefig: save confusion matrix to file (default=False).
Returns:
A labelled confusion matrix plot comparing y_true and y_pred.
Example usage:
make_confusion_matrix(y_true=test_labels, # ground truth test labels
y_pred=y_preds, # predicted labels
classes=class_names, # array of class label names
figsize=(15, 15),
text_size=10)
"""
# Create the confustion matrix
cm = confusion_matrix(y_true, y_pred)
cm_norm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis] # normalize it
n_classes = cm.shape[0] # find the number of classes we're dealing with
# Plot the figure and make it pretty
fig, ax = plt.subplots(figsize=figsize)
cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better
fig.colorbar(cax)
# Are there a list of classes?
if classes:
labels = classes
else:
labels = np.arange(cm.shape[0])
# Label the axes
ax.set(title="Confusion Matrix",
xlabel="Predicted label",
ylabel="True label",
xticks=np.arange(n_classes), # create enough axis slots for each class
yticks=np.arange(n_classes),
xticklabels=labels, # axes will labeled with class names (if they exist) or ints
yticklabels=labels)
# Make x-axis labels appear on bottom
ax.xaxis.set_label_position("bottom")
ax.xaxis.tick_bottom()
# plotting x labels vertically
plt.xticks(rotation=70, fontsize=text_size)
# Set the threshold for different colors
threshold = (cm.max() + cm.min()) / 2.
# Plot the text on each cell
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
if norm:
plt.text(j, i, f"{cm[i, j]} ({cm_norm[i, j] * 100:.1f}%)",
horizontalalignment="center",
color="white" if cm[i, j] > threshold else "black",
size=text_size)
else:
plt.text(j, i, f"{cm[i, j]}",
horizontalalignment="center",
color="white" if cm[i, j] > threshold else "black",
size=text_size)
# Save the figure to the current working directory
if savefig:
fig.savefig("confusion_matrix.png")
make_confusion_matrix(y_true=y_label, y_pred=pred_classes, classes=test_data.class_names, figsize=(100, 100))
from sklearn.metrics import classification_report
print(classification_report(y_true=y_label, y_pred=pred_classes))
precision recall f1-score support
0 0.32 0.23 0.27 250
1 0.52 0.76 0.62 250
2 0.62 0.60 0.61 250
3 0.92 0.48 0.63 250
4 0.58 0.44 0.50 250
5 0.48 0.39 0.43 250
6 0.81 0.73 0.77 250
7 0.84 0.77 0.80 250
8 0.28 0.53 0.37 250
9 0.39 0.70 0.50 250
10 0.61 0.37 0.46 250
11 0.84 0.54 0.66 250
12 0.65 0.59 0.62 250
13 0.55 0.58 0.56 250
14 0.48 0.62 0.54 250
15 0.49 0.28 0.36 250
16 0.77 0.50 0.60 250
17 0.36 0.49 0.41 250
18 0.45 0.55 0.50 250
19 0.62 0.66 0.64 250
20 0.77 0.74 0.76 250
21 0.52 0.61 0.56 250
22 0.38 0.38 0.38 250
23 0.72 0.74 0.73 250
24 0.77 0.67 0.72 250
25 0.74 0.68 0.71 250
26 0.39 0.36 0.38 250
27 0.78 0.78 0.78 250
28 0.76 0.69 0.72 250
29 0.78 0.70 0.74 250
30 0.88 0.62 0.73 250
31 0.72 0.80 0.76 250
32 0.80 0.85 0.82 250
33 0.94 0.98 0.96 250
34 0.67 0.75 0.71 250
35 0.70 0.65 0.67 250
36 0.51 0.42 0.46 250
37 0.27 0.58 0.37 250
38 0.75 0.68 0.71 250
39 0.53 0.22 0.31 250
40 0.70 0.77 0.74 250
41 0.64 0.68 0.66 250
42 0.64 0.45 0.53 250
43 0.80 0.54 0.65 250
44 0.78 0.46 0.58 250
45 0.86 0.79 0.82 250
46 0.64 0.63 0.64 250
47 0.42 0.55 0.48 250
48 0.53 0.73 0.61 250
49 0.41 0.49 0.45 250
50 0.59 0.25 0.35 250
51 0.75 0.88 0.81 250
52 0.66 0.64 0.65 250
53 0.62 0.72 0.67 250
54 0.82 0.84 0.83 250
55 0.79 0.81 0.80 250
56 0.37 0.40 0.39 250
57 0.43 0.49 0.46 250
58 0.60 0.60 0.60 250
59 0.64 0.35 0.45 250
60 0.74 0.65 0.69 250
61 0.76 0.62 0.69 250
62 0.45 0.67 0.54 250
63 0.93 0.90 0.91 250
64 0.66 0.90 0.76 250
65 0.85 0.80 0.82 250
66 0.70 0.35 0.47 250
67 0.40 0.52 0.45 250
68 0.83 0.84 0.84 250
69 0.89 0.83 0.86 250
70 0.81 0.82 0.82 250
71 0.85 0.38 0.52 250
72 0.65 0.64 0.65 250
73 0.37 0.61 0.46 250
74 0.85 0.48 0.61 250
75 0.85 0.86 0.85 250
76 0.72 0.89 0.80 250
77 0.30 0.32 0.31 250
78 0.71 0.72 0.71 250
79 0.54 0.73 0.62 250
80 0.57 0.70 0.63 250
81 0.52 0.78 0.62 250
82 0.36 0.27 0.31 250
83 0.57 0.78 0.66 250
84 0.45 0.41 0.43 250
85 0.57 0.64 0.60 250
86 0.83 0.85 0.84 250
87 0.41 0.39 0.40 250
88 0.78 0.80 0.79 250
89 0.43 0.52 0.47 250
90 0.89 0.62 0.73 250
91 0.75 0.90 0.82 250
92 0.76 0.57 0.65 250
93 0.33 0.22 0.27 250
94 0.68 0.40 0.50 250
95 0.81 0.54 0.65 250
96 0.56 0.44 0.49 250
97 0.70 0.64 0.67 250
98 0.43 0.54 0.48 250
99 0.30 0.48 0.37 250
100 0.77 0.59 0.67 250
accuracy 0.61 25250
macro avg 0.63 0.61 0.61 25250
weighted avg 0.63 0.61 0.61 25250
# Get a dictionary of classification report
classification_report_dict = classification_report(y_true=y_label, y_pred=pred_classes, output_dict=True)
class_names = test_data.class_names
# Create empty dictionary
class_f1_scores = {}
# Loop through classification report
for k, v in classification_report_dict.items():
if k == "accuracy": # Stop once we get to accuracy key
break
else:
# Add class names and F1 scores o new dictionaty
class_f1_scores[class_names[int(k)]] = v["f1-score"]
class_f1_scores
{'apple_pie': 0.2703962703962704,
'baby_back_ribs': 0.6161290322580644,
'baklava': 0.6138211382113822,
'beef_carpaccio': 0.6279683377308708,
'beef_tartare': 0.4965831435079727,
'beet_salad': 0.43171806167400884,
'beignets': 0.76890756302521,
'bibimbap': 0.8024948024948025,
'bread_pudding': 0.3658872077028886,
'breakfast_burrito': 0.5035765379113019,
'bruschetta': 0.45885286783042395,
'caesar_salad': 0.6601941747572816,
'cannoli': 0.6205450733752621,
'caprese_salad': 0.5620155038759689,
'carrot_cake': 0.5445026178010471,
'ceviche': 0.35714285714285715,
'cheese_plate': 0.6019417475728156,
'cheesecake': 0.4141414141414142,
'chicken_curry': 0.49640287769784175,
'chicken_quesadilla': 0.6434108527131783,
'chicken_wings': 0.7551020408163266,
'chocolate_cake': 0.5598526703499079,
'chocolate_mousse': 0.376,
'churros': 0.7272727272727273,
'clam_chowder': 0.7194860813704498,
'club_sandwich': 0.708595387840671,
'crab_cakes': 0.37837837837837834,
'creme_brulee': 0.781563126252505,
'croque_madame': 0.7238493723849372,
'cup_cakes': 0.7410526315789474,
'deviled_eggs': 0.7259953161592505,
'donuts': 0.7566539923954372,
'dumplings': 0.8239845261121858,
'edamame': 0.9609375000000001,
'eggs_benedict': 0.7069943289224954,
'escargots': 0.6749482401656315,
'falafel': 0.46389496717724293,
'filet_mignon': 0.3697047496790757,
'fish_and_chips': 0.7100840336134454,
'foie_gras': 0.3068181818181818,
'french_fries': 0.7352380952380952,
'french_onion_soup': 0.6627906976744187,
'french_toast': 0.5283018867924527,
'fried_calamari': 0.645933014354067,
'fried_rice': 0.577889447236181,
'frozen_yogurt': 0.8232848232848233,
'garlic_bread': 0.6370967741935485,
'gnocchi': 0.47735191637630664,
'greek_salad': 0.6127946127946128,
'grilled_cheese_sandwich': 0.4468864468864469,
'grilled_salmon': 0.3539325842696629,
'guacamole': 0.8118081180811809,
'gyoza': 0.6504065040650406,
'hamburger': 0.6691449814126393,
'hot_and_sour_soup': 0.8293650793650793,
'hot_dog': 0.7999999999999999,
'huevos_rancheros': 0.3877159309021113,
'hummus': 0.4577861163227017,
'ice_cream': 0.6024096385542169,
'lasagna': 0.45360824742268036,
'lobster_bisque': 0.6936170212765959,
'lobster_roll_sandwich': 0.6857142857142857,
'macaroni_and_cheese': 0.5410628019323671,
'macarons': 0.9142857142857143,
'miso_soup': 0.7640067911714771,
'mussels': 0.822314049586777,
'nachos': 0.46933333333333327,
'omelette': 0.4509466437177281,
'onion_rings': 0.8356435643564355,
'oysters': 0.8577319587628867,
'pad_thai': 0.8158415841584158,
'paella': 0.5248618784530387,
'pancakes': 0.6491935483870968,
'panna_cotta': 0.4613050075872534,
'peking_duck': 0.6122448979591836,
'pho': 0.8542914171656687,
'pizza': 0.7950089126559716,
'pork_chop': 0.3079922027290448,
'poutine': 0.7142857142857143,
'prime_rib': 0.6179966044142615,
'pulled_pork_sandwich': 0.6261180679785331,
'ramen': 0.6217948717948717,
'ravioli': 0.30979498861047833,
'red_velvet_cake': 0.6598984771573604,
'risotto': 0.4291666666666667,
'samosa': 0.6003752345215759,
'sashimi': 0.8379446640316205,
'scallops': 0.40163934426229503,
'seaweed_salad': 0.7913385826771654,
'shrimp_and_grits': 0.4716636197440585,
'spaghetti_bolognese': 0.7341176470588235,
'spaghetti_carbonara': 0.8173598553345389,
'spring_rolls': 0.6484018264840182,
'steak': 0.2660332541567696,
'strawberry_shortcake': 0.50126582278481,
'sushi': 0.6457831325301205,
'tacos': 0.49217002237136465,
'takoyaki': 0.6708595387840671,
'tiramisu': 0.48042704626334526,
'tuna_tartare': 0.3726708074534162,
'waffles': 0.6681715575620767}
# turn f1 scores into dataframe for visualization
import pandas as pd
f1_scores = pd.DataFrame({"class_names": list(class_f1_scores.keys()),
"f1-score": list(class_f1_scores.values())}).sort_values("f1-score", ascending=False)
f1_scores[:10]
| class_names | f1-score | |
|---|---|---|
| 33 | edamame | 0.960938 |
| 63 | macarons | 0.914286 |
| 69 | oysters | 0.857732 |
| 75 | pho | 0.854291 |
| 86 | sashimi | 0.837945 |
| 68 | onion_rings | 0.835644 |
| 54 | hot_and_sour_soup | 0.829365 |
| 32 | dumplings | 0.823985 |
| 45 | frozen_yogurt | 0.823285 |
| 65 | mussels | 0.822314 |
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(12, 25))
scores = ax.barh(range(len(f1_scores)), f1_scores["f1-score"].values)
ax.set_yticks(range(len(f1_scores)))
ax.set_yticklabels(f1_scores["class_names"])
ax.set_xlabel("F1-score")
ax.set_title("F1-score for 101 food classes")
ax.bar_label(scores, fmt="%.2f")
ax.invert_yaxis();
Steps:
# Create a function to load and prepare images
def load_and_prep_image(filename, img_size=224, scale=True):
"""
reads in an image form filename, turns it into a tensor and reshapes into specified shape
Args:
filename (str): path to target image
img_shape (int): height/width dimentions of target image size
scale (bool): scale pixel values from 0-255 to 0-1 or not
Returns:
Image tensor of shape (img_shape, img_shape, 3)
"""
# Read in the image
img = tf.io.read_file(filename=filename)
# Decode image into tensor
img = tf.io.decode_image(img, channels=3)
# Resize the image
img = tf.image.resize(img, [img_size, img_size])
# Scaling
if scale:
# Rescale the image
return img/255.
else:
return img
# visualize test images
# Make preds on series of random images
import os
import random
plt.figure(figsize=(17, 10))
for i in range(3):
# Choose a random image from random class
class_name = random.choice(class_names)
filename = random.choice(os.listdir(test_dir + "/" + class_name))
filepath = test_dir + "/" + class_name + "/" + filename
# Load the images and make predictions
img = load_and_prep_image(filepath, scale=False)
img_expanded = tf.expand_dims(img, axis=0)
# pred_prob = model.predict(img_expanded) # get perdiction probability array
pred_prob = loaded_model.predict(img_expanded) # get perdiction probability array
pred_class = class_names[pred_prob.argmax()] # get highest prediction probability index
# Plot the images
plt.subplot(1, 3, i+1)
plt.imshow(img/255)
if class_name == pred_class: # if predicted class matches, truth class, make text green
title_color = "g"
else:
title_color = "r"
plt.title(f"Actual class: {class_name}, pred: {pred_class}, prob: {pred_prob.max():.2f}", c=title_color)
plt.axis(False);
1/1 [==============================] - 1s 1s/step 1/1 [==============================] - 0s 25ms/step 1/1 [==============================] - 0s 25ms/step
# get all of the files from test_data
filepaths = []
for filepath in test_data.list_files("101_food_classes_10_percent/test/*/*.jpg", shuffle=False):
filepaths.append(filepath.numpy())
filepaths[:10]
[b'101_food_classes_10_percent/test/apple_pie/1011328.jpg', b'101_food_classes_10_percent/test/apple_pie/101251.jpg', b'101_food_classes_10_percent/test/apple_pie/1034399.jpg', b'101_food_classes_10_percent/test/apple_pie/103801.jpg', b'101_food_classes_10_percent/test/apple_pie/1038694.jpg', b'101_food_classes_10_percent/test/apple_pie/1047447.jpg', b'101_food_classes_10_percent/test/apple_pie/1068632.jpg', b'101_food_classes_10_percent/test/apple_pie/110043.jpg', b'101_food_classes_10_percent/test/apple_pie/1106961.jpg', b'101_food_classes_10_percent/test/apple_pie/1113017.jpg']
# Create a dataframe of different parameters
pred_df = pd.DataFrame({"img_path": filepaths,
"y_true": y_label,
"y_pred": pred_classes,
"pred_conf": preds_probs.max(axis=1), # Get the maximum prediction probability value
"y_true_classname": [class_names[i] for i in y_label],
"y_pred_classname": [class_names[i] for i in pred_classes]})
pred_df
| img_path | y_true | y_pred | pred_conf | y_true_classname | y_pred_classname | |
|---|---|---|---|---|---|---|
| 0 | b'101_food_classes_10_percent/test/apple_pie/1... | 0 | 0 | 0.213262 | apple_pie | apple_pie |
| 1 | b'101_food_classes_10_percent/test/apple_pie/1... | 0 | 0 | 0.828701 | apple_pie | apple_pie |
| 2 | b'101_food_classes_10_percent/test/apple_pie/1... | 0 | 0 | 0.716567 | apple_pie | apple_pie |
| 3 | b'101_food_classes_10_percent/test/apple_pie/1... | 0 | 8 | 0.197051 | apple_pie | bread_pudding |
| 4 | b'101_food_classes_10_percent/test/apple_pie/1... | 0 | 8 | 0.460519 | apple_pie | bread_pudding |
| ... | ... | ... | ... | ... | ... | ... |
| 25245 | b'101_food_classes_10_percent/test/waffles/942... | 100 | 100 | 0.933011 | waffles | waffles |
| 25246 | b'101_food_classes_10_percent/test/waffles/954... | 100 | 16 | 0.576720 | waffles | cheese_plate |
| 25247 | b'101_food_classes_10_percent/test/waffles/961... | 100 | 100 | 0.369479 | waffles | waffles |
| 25248 | b'101_food_classes_10_percent/test/waffles/970... | 100 | 94 | 0.279318 | waffles | strawberry_shortcake |
| 25249 | b'101_food_classes_10_percent/test/waffles/971... | 100 | 100 | 0.441311 | waffles | waffles |
25250 rows × 6 columns
# Find which predictions are wrong
pred_df["pred_correct"] = pred_df["y_true"] == pred_df["y_pred"]
pred_df.head()
| img_path | y_true | y_pred | pred_conf | y_true_classname | y_pred_classname | pred_correct | |
|---|---|---|---|---|---|---|---|
| 0 | b'101_food_classes_10_percent/test/apple_pie/1... | 0 | 0 | 0.213262 | apple_pie | apple_pie | True |
| 1 | b'101_food_classes_10_percent/test/apple_pie/1... | 0 | 0 | 0.828701 | apple_pie | apple_pie | True |
| 2 | b'101_food_classes_10_percent/test/apple_pie/1... | 0 | 0 | 0.716567 | apple_pie | apple_pie | True |
| 3 | b'101_food_classes_10_percent/test/apple_pie/1... | 0 | 8 | 0.197051 | apple_pie | bread_pudding | False |
| 4 | b'101_food_classes_10_percent/test/apple_pie/1... | 0 | 8 | 0.460519 | apple_pie | bread_pudding | False |
# Sort dataframe to have most wrong predictions at top
top_100_wrong = pred_df[pred_df["pred_correct"] == False].sort_values("pred_conf", ascending=False)[:100]
top_100_wrong.head(20)
| img_path | y_true | y_pred | pred_conf | y_true_classname | y_pred_classname | pred_correct | |
|---|---|---|---|---|---|---|---|
| 23797 | b'101_food_classes_10_percent/test/sushi/16593... | 95 | 86 | 0.999345 | sushi | sashimi | False |
| 18001 | b'101_food_classes_10_percent/test/pancakes/10... | 72 | 67 | 0.998549 | pancakes | omelette | False |
| 191 | b'101_food_classes_10_percent/test/apple_pie/3... | 0 | 31 | 0.997635 | apple_pie | donuts | False |
| 17855 | b'101_food_classes_10_percent/test/paella/2314... | 71 | 65 | 0.997257 | paella | mussels | False |
| 8794 | b'101_food_classes_10_percent/test/escargots/1... | 35 | 41 | 0.996961 | escargots | french_onion_soup | False |
| 10847 | b'101_food_classes_10_percent/test/fried_calam... | 43 | 68 | 0.996513 | fried_calamari | onion_rings | False |
| 15142 | b'101_food_classes_10_percent/test/lobster_bis... | 60 | 64 | 0.996350 | lobster_bisque | miso_soup | False |
| 21810 | b'101_food_classes_10_percent/test/scallops/17... | 87 | 29 | 0.996283 | scallops | cup_cakes | False |
| 7194 | b'101_food_classes_10_percent/test/croque_mada... | 28 | 7 | 0.994985 | croque_madame | bibimbap | False |
| 23631 | b'101_food_classes_10_percent/test/strawberry_... | 94 | 83 | 0.993791 | strawberry_shortcake | red_velvet_cake | False |
| 11642 | b'101_food_classes_10_percent/test/garlic_brea... | 46 | 10 | 0.993379 | garlic_bread | bruschetta | False |
| 5849 | b'101_food_classes_10_percent/test/churros/254... | 23 | 68 | 0.992862 | churros | onion_rings | False |
| 10013 | b'101_food_classes_10_percent/test/french_frie... | 40 | 43 | 0.992745 | french_fries | fried_calamari | False |
| 10880 | b'101_food_classes_10_percent/test/fried_calam... | 43 | 68 | 0.992740 | fried_calamari | onion_rings | False |
| 10854 | b'101_food_classes_10_percent/test/fried_calam... | 43 | 68 | 0.991826 | fried_calamari | onion_rings | False |
| 722 | b'101_food_classes_10_percent/test/baklava/640... | 2 | 85 | 0.991008 | baklava | samosa | False |
| 14691 | b'101_food_classes_10_percent/test/ice_cream/3... | 58 | 79 | 0.990902 | ice_cream | prime_rib | False |
| 12487 | b'101_food_classes_10_percent/test/grilled_che... | 49 | 25 | 0.990754 | grilled_cheese_sandwich | club_sandwich | False |
| 15359 | b'101_food_classes_10_percent/test/lobster_rol... | 61 | 53 | 0.990743 | lobster_roll_sandwich | hamburger | False |
| 5114 | b'101_food_classes_10_percent/test/chicken_win... | 20 | 38 | 0.990371 | chicken_wings | fish_and_chips | False |
# Visualize wrong predictions
images_to_view = 9
start_index = 10
plt.figure(figsize=(30, 20))
for i, row in enumerate(top_100_wrong[start_index:start_index + images_to_view].itertuples()):
plt.subplot(3, 3, i+1)
img = load_and_prep_image(row[1], scale=False)
_, _, _, _, pred_prob, y_true_classname, y_pred_classname, _ = row # skipping few values
plt.imshow(img/255)
plt.title(f"actual: {y_true_classname}, pred: {y_pred_classname}, prob: {pred_prob}")
plt.axis(False);
!python -m wget https://storage.googleapis.com/ztm_tf_course/food_vision/custom_food_images.zip
--2023-03-13 22:28:29-- https://storage.googleapis.com/ztm_tf_course/food_vision/custom_food_images.zip Resolving storage.googleapis.com (storage.googleapis.com)... 142.251.41.80, 172.217.165.16, 142.251.32.80, ... Connecting to storage.googleapis.com (storage.googleapis.com)|142.251.41.80|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 13192985 (13M) [application/zip] Saving to: ‘custom_food_images.zip’ custom_food_images. 100%[===================>] 12.58M 29.7MB/s in 0.4s 2023-03-13 22:28:30 (29.7 MB/s) - ‘custom_food_images.zip’ saved [13192985/13192985]
unzip_data("custom_food_images.zip")
walk_through_dir("custom_food_images")
There are 0 directories and 6 images in 'custom_food_images'.
# Get custom food images filepaths
custom_food_images = ["custom_food_images/" + img_path for img_path in os.listdir("custom_food_images")]
custom_food_images
['custom_food_images/chicken_wings.jpeg', 'custom_food_images/ramen.jpeg', 'custom_food_images/sushi.jpeg', 'custom_food_images/hamburger.jpeg', 'custom_food_images/pizza-dad.jpeg', 'custom_food_images/steak.jpeg']
# make predictions and plot custom food images
for img in custom_food_images:
img = load_and_prep_image(img, scale=False)
pred_prob = model.predict(tf.expand_dims(img, axis=0))
pred_class = class_names[pred_prob.argmax()]
# Plot the information
plt.figure()
plt.imshow(img/255.)
plt.title(f"pred: {pred_class}, prob: {pred_prob.max():.2f}")
plt.axis(False);
1/1 [==============================] - 1s 792ms/step 1/1 [==============================] - 0s 28ms/step 1/1 [==============================] - 0s 29ms/step 1/1 [==============================] - 0s 24ms/step 1/1 [==============================] - 0s 23ms/step 1/1 [==============================] - 0s 23ms/step
